Changes in modal parameters of a steel truss bridge due to artificial damage

Kim, C.W.; Chang, Kai-Chun; Kitauchi, S.; McGetrick, Patrick; Hashimoto, Kenro; Sugiura, Kunitomo

doi:10.1201/b16387-541

Cited by 5 publications

(9 citation statements)

References 9 publications

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“…In the first case, when the condition of bridge goes from the undamaged state to damage 2 (DMG 2) the average frequency 1 decreases from 3. decreases from 2.95 Hz to 2.90 Hz. In the present study, good instantaneous frequencies results were obtained in contrast with reference analysis developed by Kim et al (2014).…”

Section: δ4contrasting

confidence: 49%

“…5. To evaluate the damage identification capability of the vibration parameters assessed under vehicle induced excitation, the percentage variation observed at the damage location for condition states; DMG2, RCV & DMG3 are given in Table 5 and can be compared against modal frequency changes for each damage scenario in Table 6, courtesy of Kim et al (2014). From the comparison, all of the vibration parameters assessed appear to outperform the modal frequency changes in this regard, with AIVI's percentage differences producing the best overall.…”

Section: Discussionmentioning

confidence: 99%

“…Table 6. Modal frequency variation (Kim et al, 2014) Mode DMG2 RCV DMG3 1 st B. Mode -2.67% -0.13% +0.31% 2 nd B. Mode +0.20% -0.25% -5.67% 3 rd B. Mode -0.21% -0.87% -9.05% 4 th B. Mode +0.22% -0.72% -6.87% 5 th B. Mode +0.58% +0.19% -0.16%…”

Section: Discussionmentioning

confidence: 99%

“…For the undamaged condition, Figure 9 shows an example of Hilbert-Huang Spectrum that is obtained for the first 3 IMFs of Sensor 1 in the steel truss bridge described in the case of study. The time-frequency variation shows energy concentration around 3Hz, 6.8Hz and 13.3Hz, which represent the first, second and fifth vibrational mode as per Kim, et al (2014). However, considering only this information is difficult to ascertain changes in structural behaviour.…”

Section: Vehicle-induced Excitation -Steel Truss Bridgementioning

confidence: 99%

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Non-modal vibration-based methods for bridge damage identification

Delgadillo

Casas

2019

Structure and Infrastructure Engineering

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Many methods of damage identification in bridge structures have focused on the use of either numerical models, modal parameters or non-destructive damage tests as a means of condition assessment. These techniques can often be very effective but can also suffer from specific pitfalls such as, numerical model calibration issues for nonlinear and inelastic behaviour, modal parameter sensitivity to environmental and operational conditions and bridge usage restrictions for nondestructive testing. The present paper covers alternative approaches to damage identification of bridge structures using empirical parameters applied to measured vibration response data obtained from two field experiments of progressively damaged bridges subjected to ambient and vehicle induced excitation, respectively. Numerous non-modal vibration-based damage features are detailed and selected for the assessment of either the ambient or vehicle induced excitation data based on their inherent properties. The results of the application to 2 real bridges, one under ambient vibration and the other of forced vibration demonstrate the robustness of the proposed damage features for damage identification using measurements of ambient and vehicle excitations. Moreover, this investigation has demonstrated that the novel empirical vibration parameters assessed are suitable for damage detection, localization and quantification.

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Section: δ4contrasting

confidence: 49%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Vehicle-induced Excitation -Steel Truss Bridgementioning

confidence: 99%

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Non-modal vibration-based methods for bridge damage identification

Delgadillo

Casas

2019

Structure and Infrastructure Engineering

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“…Kanta and Samit proposed a new approach to detect and quantify damage in railway truss bridges; damage localization is conducted using an approach based on mode shape and its derivative, and the sensitivity‐based Bayesian damage identification algorithm is applied to evaluate the damage extent; the method was validated through the simulation and laboratory experiment; however, the reliability of the approach suffers when data are contaminated by high level of noise. Kim et al analyzed the influence of damage on changes in modal parameters of a real truss bridge; the sensitivity and precision of different modal parameters under different damage cases are evaluated on field test data.…”

Section: Recent Progress On Damage Identification Methods For Truss Bmentioning

confidence: 99%

Recent progress and future trends on damage identification methods for bridge structures

Chatzi

Sim

et al. 2019

Struct Control Health Monit

211

112

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Summary Damage identification forms a key objective in structural health monitoring. Several state‐of‐the‐art review papers regarding progress in this field up to 2011 have been published. This paper summarizes the recent progress between 2011 and 2017 in the area of damage identification methods for bridge structures. This paper is organized based on the classification of bridge infrastructure in terms of fundamental structural systems, namely, beam bridges, truss bridges, arch bridges, cable‐stayed bridges, and suspension bridges. The overview includes theoretical developments, enhanced simulation attempts, laboratory‐scale implementations, full‐scale validation, and the summary for each type of bridges. Based on the offered review, some challenges, suggestions, and future trends in damage identification are proposed. The work can be served as a basis for both academics and practitioners, who seek to implement damage identification methods in next‐generation structural health monitoring systems.

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Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Delgadillo

Casas

2022

Structural Contr & Hlth

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Structural health monitoring field is growing in the use of more modern techniques and tools in order to identify damages in civil structures. The improvements in signal processing techniques and data mining have, recently, been employed due to their powerful computational ability to detect damage in bridges. Despite the majority of researchers have been studying laboratory-scale implementations and theoretical developments, the limited data to identify structural faults in real bridges are still a problem. The current study presents a novel approach for damage identification by using two improved methods such as decomposition techniques and machine learning algorithms. Since the data obtained from the traffic vibration in real bridges are non-linear and time varying, the Hilbert-Huang transform is used to process the vibration data.

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Changes in modal parameters of a steel truss bridge due to artificial damage

Cited by 5 publications

References 9 publications

Non-modal vibration-based methods for bridge damage identification

Non-modal vibration-based methods for bridge damage identification

Recent progress and future trends on damage identification methods for bridge structures

Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

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